Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

A Light-Weight Cropland Mapping Model Using Satellite Imagery

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine-learning-based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions, namely Iran, Mozambique, Sri-Lanka, and Sudan, where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction, and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 and 0.98 across the four test regions at hand.

Assessing the impact of urbanization on groundwater quality of lahore region, Pakistan

Land use land cover (LULC) dynamics is an important aspect of environmental studies. Lahore is one of the wide-ranging urban cities in the world experiencing rapid development in the form of unplanned urban growth and industrialization, which leads to many adverse consequences. This research focuses on the study of spatio-temporal variability of urbanization and its impact on the water quality index (WQI) in Lahore city using remote sensing (RS) and geographical information systems (GIS). Landsat images (Landsat 7 ETM⁺, Landsat 8 OLI) between 2005 to 2021 were used to observe the changes in urban growth over seventeen years. GIS is used to create the LULC, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) maps, to study the urbanization impact on the WQI. The results of this study indicate that the groundwater quality of metropolitan Lahore city has significantly dropped within 17 years. The extent of the built-up area has been expanded from 22.4% to 953.04% with an increase in the poor WQI area from 1.95% to 37.89%, reveals a general decline in groundwater quality with urbanization. Indeed, the trends observed by the linear regression modelling showed a positive and negative correlation (R² = 0.67 and -0.74) of WQI with % of urban and vegetation areas respectively. GIS and RS tools have been found effective in assessing spatio-temporal phenomena of urbanization and its impact on groundwater quality. Furthermore, this research would be very helpful in making decisions for managing groundwater resources and illegal urban expansion in Lahore city.

Multi-temporal assessment of land surface temperatures as an indicator of land use/cover changes and climate variability in the Develi Basin, Turkey

Land surface temperature (LST) is an important parameter that reflects land surface processes of water and energy balance and has been used in assessment of land use/cover changes. However, the use of LST in monitoring changes in non-urban areas such as agricultural areas and wetlands is still limited. In this study, we aim to determine the spatial and temporal changes in LST in a semi-arid agricultural basin in Turkey (Develi Basin), where land use/cover and climatic conditions showed considerable variability since 1980s. Irrigated agriculture have expanded in the basin since 1987, after the construction of a large irrigation project. The basin hosts an internationally important wetland, called the Sultan Marshes, affected negatively by irrigation expansion. The study covers a 39-year period from 1984 to 2022. Four Landsat Thematic Mapper (TM) images acquired in 1984, 1987, 2003, and 2007 and two Landsat 8 OLI/TIRS images acquired in 2014 and 2022 were used in the analyses. The land use/cover changes were evaluated based on Normalized Difference Vegetation Index (NDVI). LST was estimated through top-of-atmosphere brightness temperature from thermal bands of Landsat images. Climate variability from 2014 to 2022 was analyzed with statistical methods. The results indicated that Develi Basin faced both spatial and temporal land use/cover changes. The area covered with natural steppe vegetation and water bodies decreased in the basin. In contrast, the sparsely and densely vegetated soil covers, which mostly denote agricultural areas, increased. Changes in LST values were observed from 1984 to 2022 as a result of climatic factors and land use/cover changes. LST changes were variable across different land use/cover types; LST decreased in irrigated areas and increased in lakes that went dry over years. LST changes proved useful for evaluating land use/cover changes and climatic variations in agricultural basins.

Daytime warming strengthened delaying effect of precipitation on end of the vegetation growing season on the Tibetan Plateau

Many studies have reported that daytime warming advances the end of the vegetation growing season (EOS) in arid and semi-arid ecosystems in the northern middle latitudes. This finding, however, seems to contradict the fact that low temperature constrains alpine vegetation activity. Using EOS from 1982 to 2015 retrieved from satellite observations, we show that daytime warming could facilitate a delay in EOS on the Tibetan Plateau, the world's largest and highest alpine region, with a dry and cold climate. Our analysis revealed a positive partial correlation (R_EOS-Tmax) between EOS and preseason mean daily maximum temperature (T_max) on 57 % of the Plateau in wetter years, but on only 41 % of it in drier years. At a regional level, R_EOS-Tmax was 0.69 (P < 0.05, t-test) during wetter years and -0.56 (P = 0.11) during drier years, indicating that daytime warming could directly delay EOS on the Plateau. On the other hand, we found a positive partial correlation (R_EOS-Prec) between EOS and preseason cumulative precipitation on 62 % of the Plateau during warmer years, but on only 47 % during colder years. At a regional level, R_EOS-Prec was 0.68 (P < 0.05) during warmer years and - 0.28 (P = 0.46) during colder years. Moreover, R_EOS-Prec increased on 60 % of the Plateau under increasing T_max during 1982-2015, suggesting that daytime warming facilitates a delay in EOS on the Tibetan Plateau by regulating the effect of precipitation on EOS. Thus, to improve autumn phenology models in this region, researchers should consider the interactive effects of temperature and precipitation on EOS.

A Near Four-Decade Time Series Shows the Hawaiian Islands Have Been Browning Since the 1980s

The Hawaiian Islands have been identified as a global biodiversity hotspot. We examine the Normalized Difference Vegetation Index (NDVI) using Climate Data Records products (0.05 × 0.05°) to identify significant differences in NDVI between neutral El Niño-Southern Oscillation years (1984, 2019) and significant long-term changes over the entire time series (1982-2019) for the Hawaiian Islands and six land cover classes. Overall, there has been a significant decline in NDVI (i.e., browning) across the Hawaiian Islands from 1982 to 2019 with the islands of Lāna'i and Hawai'i experiencing the greatest decreases in NDVI (≥44%). All land cover classes significantly decreased in NDVI for most months, especially during the wet season month of March. Native vegetation cover across all islands also experienced significant declines in NDVI, with the leeward, southwestern side of the island of Hawai'i experiencing the greatest declines. The long-term trends in the annual total precipitation and annual mean Palmer Drought Severity Index (PDSI) for 1982-2019 on the Hawaiian Islands show significant concurrent declines. Primarily positive correlations between the native ecosystem NDVI and precipitation imply that significant decreases in precipitation may exacerbate the decrease in NDVI of native ecosystems. NDVI-PDSI correlations were primarily negative on the windward side of the islands and positive on the leeward sides, suggesting a higher sensitivity to drought for leeward native ecosystems. Multi-decadal time series and spatially explicit data for native landscapes provide natural resource managers with long-term trends and monthly changes associated with vegetation health and stability.

Monitoring Spatiotemporal Vegetation Response to Drought Using Remote Sensing Data

Environmental factors such as drought significantly influence vegetation growth, coverage, and ecosystem functions. Hence, monitoring the spatiotemporal vegetation responses to drought in a high temporal and adequate spatial resolution is essential, mainly at the local scale. This study was conducted to investigate the aspatial and spatial relationships between vegetation growth status and drought in the southeastern South Dakota, USA. For this purpose, Landsat 8 OLI images from the months of April through September for the years 2016-2021, with cloud cover of less than 10%, were acquired. After that, radiometric calibration and atmospheric correction were performed on all of the images. Some spectral indices were calculated using the Band Math toolbox in ENVI 5.3 (Environment for Visualizing Images v. 5.3). In the present study, the extracted spectral indices from Landsat 8 OLI images were the Normalized Difference Vegetation Index (NDVI) and the Normalized Multiband Drought Index (NMDI). The results showed that the NDVI values for the month of July in different years were at maximum value at mostly pixels. Based on the statistical criteria, the best regression models for explaining the relationship between NDVI and NMDI_Soil were polynomial order 2 for 2016 to 2019 and linear for 2021. The developed regression models accounted for 96.7, 95.7, 96.2, 88.4, and 32.2% of vegetation changes for 2016, 2017, 2018, 2019, and 2021, respectively. However, there was no defined trend between NDVI and NMDI_Soil observed in 2020. In addition, pixel-by-pixel analyses showed that drought significantly impacted vegetation coverage, and 69.6% of the pixels were negatively correlated with the NDVI. It was concluded that the Landsat satellite images have potential information for studying the relationships between vegetation growth status and drought, which is the primary step in site-specific management.

Multi-scale spatial analysis of satellite-retrieved surface evapotranspiration in Beijing, a rapidly urbanizing region under continental monsoon climate

As one of the important components of hydrological cycle, evapotranspiration spatial distribution is of great significance to regional water resources planning and rational utilization. This research used Surface Energy Balance System model to estimate the daily evapotranspiration (ET) in Beijing based on Landsat 8 observations. Results showed that the daily ET in Beijing ranged from 3.469 to 5.474 mm/day. ET is known to decrease with the increase of land surface temperature (LST) and to increase with the increase of Normalized Difference Vegetation Index (NDVI). NDVI primarily decreased from the northwest to the southeast. When the NDVI value was 0.4-0.6, the average ET peaked at 4.88 mm/day, and then slightly decreased by 3.7%. The coefficient of determination of NDVI (0.95) was much greater than that of LST (0.30) upon linear fitting, showing LST was not the main factor controlling ET in Beijing. In contrast to the linear fitting results, the spatial correlation between LST and ET is more significant than that between NDVI and ET in the global bivariate spatial analysis, where the absolute value of global bivariate Moran's I of LST (0.51) was higher than that of NDVI (0.21) at a resolution of 150 m. And the univariate spatial autocorrelation indices of LST, ET, and NDVI equaled 0.84, 0.65, and 0.51, respectively. Furthermore, the complex spatial distribution pattern of variables could significantly affect the correlation analysis results. Local bivariate spatial analysis showed that over 60% of the Beijing area had a significant correlation, of which the negative correlation area of LST accounted for about 85%, and the positive correlation area of NDVI accounted for 74%. By improving the correlation analysis accuracy, the regional conditions for the establishment of correlation analysis results were clarified from the overall correlation analysis results.

Association of individual green space exposure with the incidence of asthma and allergic rhinitis: a systematic review and meta-analysis

The association between allergic respiratory diseases, such as asthma and allergic rhinitis (AR), and green space (GS) remains controversial. Our study aimed to summarize and synthesize the association between individual GS exposure and the incidence of asthma/AR. We systematically summarized the qualitative relationship between GS exposure and asthma and AR. The pooled odds ratio (OR) with 95% confidence intervals (CIs) was used to estimate the effect of the Normalized Difference Vegetation Index (NDVI) on asthma and AR. A total of 21 studies were included for systematic review, and 8 of them underwent meta-analysis. In the meta-analysis of current asthma, the 0 < radius ≤ 100 m group, 100 < radius ≤ 300 m group, and 500 < radius ≤ 1000 m group presented weak negative associations between the NDVI and current asthma. For ever asthma, slight positive associations existed in the 0 < radius ≤ 100 m group and 300 < radius ≤ 500 m group. In addition, the NDVI might slightly reduce the risk of AR in radius of 100 m and 500 m. Our findings suggest that the effects of GS exposure on asthma and AR were not significant. Differences in GS measurements, disease diagnoses and adjusted confounders across studies may have an impact on the results. Subsequent studies should consider potential confounding factors and use more accurate GS exposure measurements to better understand the impact of GS exposure on respiratory disease in the population.

Effectiveness of vegetation indices and UAV-multispectral imageries in assessing the response of hybrid maize (Zea mays L.) to water deficit stress under field environment

Unmanned aerial vehicles (UAVs) equipped with multi-sensors are one of the most innovative technologies for measuring plant health and predicting final yield in field conditions, especially in the water deficit situation in rain-deprived regions. The objective of this investigation was to evaluate the individual plant and canopy-level measurements using UAV imageries in three different genotypes, Suwan4452 (drought-tolerant), Pac339, and S7328 (drought-sensitive) of maize (Zea mays L.) at vegetative and reproductive stages under WW (well-watered) and WD (water deficit) conditions. At the vegetative stage, only CWSI (crop water stress index) of Pac339 and S7328 under WD increased significantly by 1.86- and 1.69-fold over WW, whereas the vegetation indices (EVI2 (Enhanced Vegetation Index 2), OSAVI (Optimized Soil-Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), and NDVI (Normalized Difference Vegetation Index)) derived from UAV multi-sensors did not vary. At the reproductive stage, CWSI in drought-sensitive genotype (S7328) under WD increased by 1.92-fold over WW. All the vegetation indices (EVI2, OSAVI, GNDVI, NDRE, and NDVI) of Pac339 and S7328 under WD decreased when compared with those of Suwan4452. NDVI derived from GreenSeeker^® handheld and NDVI from UAV data was closely related (R² = 0.5924). An increase in leaf temperature (T_leaf) and reduction in NDVI of WD stressed maize plants was observed (R² = 0.5829) leading to yield loss (R² = 0.5198). In summary, a close correlation was observed between the physiological data of individual plants and vegetation indices of canopy level (collected using a UAV platform) in drought-sensitive genotypes of maize crops under WD conditions, thus indicating its effectiveness in the classification of drought-tolerant genotypes.

Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India

This study examines the trends in MODIS/TERRA derived Normalized Difference Vegetation Index (NDVI) and its correlation with Land Surface Temperature (LST), Soil Moisture (SM), and precipitation over Gautam Buddh Nagar (India), during the period 2005-2018. The region have a sub-humid and quite moderate climate, scattered into cultivable land, forest and fast growing urbanization zone, making it suitable for monitoring vegetation trends and its accompanying factors. The NDVI-derived vegetation growth patterns over the study region of District Gautam Buddh Nagar, illustrate vigorous seasonal cycles, and interannual variations. The correlation between NDVI, and LST (- 0.45) was observed to be higher than the correlation of NDVI with SM (r = 0.43), and precipitation (r = 0.341), suggesting NDVI as more sensitive to LST as compare to SM, and precipitation, while SM shows the worthy positive correlation (r = 0.63) with the precipitation. On a seasonal basis, NDVI shows high values during winter (0.45 ± 0.02) followed by monsoon (0.44 ± 0.04), post-monsoon (0.41 ± 0.02), and pre-monsoon (0.37 ± 0.04). This study also aims to determine the phase wise status of NDVI and associated parameters.

Satellite data and machine learning reveal the incidence of late frost defoliations on Iberian beech forests

Climate warming is driving an advance of leaf unfolding date in temperate deciduous forests, promoting longer growing seasons and higher carbon gains. However, an earlier leaf phenology also increases the risk of late frost defoliation (LFD) events. Compiling the spatiotemporal patterns of defoliations caused by spring frost events is critical to unveil whether the balance between the current advance in leaf unfolding dates and the frequency of LFD occurrence is changing and represents a threaten for the future viability and persistence of deciduous forests. We combined satellite imagery with machine learning techniques to reconstruct the spatiotemporal patterns of LFD events for the 2003-2018 period in the Iberian range of European beech (Fagus sylvatica), at the drier distribution edge of the species. We used MODIS Vegetation Index Products to generate a Normalized Difference Vegetation Index (NDVI) time series for each 250 × 250 m pixel in a total area of 1,013 km² (16,218 pixels). A semi-supervised approach was used to train a machine learning model, in which a binary classifier called Support Vector Machine with Global Alignment Kernel was used to differentiate between late frost and non-late frost pixels. We verified the obtained estimates with photointerpretation and existing beech tree-ring chronologies to iteratively improve the model. Then, we used the model output to identify topographical and climatic factors that determined the spatial incidence of LFD. During the study period, LFD was a low recurrence phenomenon that occurred every 15.2 yr on average and showed high spatiotemporal heterogeneity. Most LFD events were condensed in 5 yr and clustered in western forests (86.5% in one-fifth of the pixels) located at high elevation with lower than average precipitation. Elevation and longitude were the major LFD risk factors, followed by annual precipitation. The synergistic effects of increasing drought intensity and rising temperature combined with more frequent late frost events may determine the future performance and distribution of beech forests. This interaction might be critical at the beech drier range edge, where the concentration of LFD at high elevations could constrain beech altitudinal shifts and/or favor species with higher resistance to late frosts.

Tracking rates of postfire conifer regeneration vs. deciduous vegetation recovery across the western United States

Postfire shifts in vegetation composition will have broad ecological impacts. However, information characterizing postfire recovery patterns and their drivers are lacking over large spatial extents. In this analysis, we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of postfire, dual-season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally specific drivers of postfire rates of NDVI recovery. Rates of postfire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n = 230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in postfire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track postfire forest recovery across the western United States. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Smokey the Beaver: beaver-dammed riparian corridors stay green during wildfire throughout the western United States

Beaver dams are gaining popularity as a low-tech, low-cost strategy to build climate resiliency at the landscape scale. They slow and store water that can be accessed by riparian vegetation during dry periods, effectively protecting riparian ecosystems from droughts. Whether or not this protection extends to wildfire has been discussed anecdotally but has not been examined in a scientific context. We used remotely sensed Normalized Difference Vegetation Index (NDVI) data to compare riparian vegetation greenness in areas with and without beaver damming during wildfire. We include data from five large wildfires of varying burn severity and dominant landcover settings in the western United States in our analysis. We found that beaver-dammed riparian corridors are relatively unaffected by wildfire when compared to similar riparian corridors without beaver damming. On average, the decrease in NDVI during fire in areas without beaver is 3.05 times as large as it is in areas with beaver. However, plant greenness rebounded in the year after wildfire regardless of beaver activity. Thus, we conclude that, while beaver activity does not necessarily play a role in riparian vegetation post-fire resilience, it does play a significant role in riparian vegetation fire resistance and refugia creation.

Stress as a facilitator? Territorial male impala have higher glucocorticoid levels than bachelors

Territoriality is a common behavioural adaptation, widespread among ungulates. Here, we tested the hypothesis that territorial individuals have higher glucocorticoid concentrations than non-territorial bachelors, in wild impala (Aepyceros melampus) in the Serengeti ecosystem. We also investigated how the relationship between territoriality and glucocorticoid levels is influenced by environmental context, specifically, food quality, population density (i.e., territory defence intensity), and herd size (i.e., mate defence effort). We collected 139 faecal samples over 4 years and analysed these for faecal glucocorticoid metabolites (FGMs). We used Normalised Difference Vegetation Index (NDVI) as a proxy for food quality, and population density was based on aerial surveys. Territorial males had, on average, higher FGM concentrations than bachelors. Increased food quality did not affect FGM levels in territorial males, but decreased FGM levels in bachelors by 78%. Greater population density increased FGM levels by 47%, but this effect was not different between territorial and bachelor males. Herd size did not affect FGM levels in territorial males. While elevated GC levels are often suggested to be repercussions of being territorial, our findings support the hypothesis that elevated GC levels may be beneficial and act as a facilitator of a male's reproductive potential. The elevated GC levels may increase the ability of territorial males to maintain a territory by increasing energy mobilisation and metabolic rate, ultimately increasing their reproductive fitness. Appreciating that long-term increases in GC levels are not simply costly but may have an adaptive, potentially facilitating role in an animal's life history is key to understanding HPA-axis reactivity and its potential in eco-physiological studies.

Using satellite-derived estimates of plant phenological rhythms to predict sage-grouse nesting chronology

The "green wave" hypothesis posits that during spring consumers track spatial gradients in emergent vegetation and associated foraging opportunities. This idea has largely been invoked to explain animal migration patterns, yet the general phenomenon underlies trends in vertebrate reproductive chronology as well. We evaluated the utility of this hypothesis for predicting spatial variation in nest initiation of greater sage-grouse (Centrocerus urophasianus), a species of conservation concern in western North America. We used the Normalized Difference Vegetation Index (NDVI) to map the green wave across elevation and then compiled dates and locations of >450 sage-grouse nests from 20 study sites (2000-2014) to model nest initiation as a function of the start of the growing season (SOS), defined here as the maximum daily rate of increase in NDVI. Individual sites were drawn from three ecoregions, distributed over 4.5° latitude, and spanning 2,300 m in elevation, which captured the climatic, edaphic, and floristic diversity of sagebrush ecosystems in the southern half of current sage-grouse range. As predicted, SOS displayed a significant, positive relationship with elevation, occurring 1.3 days later for each 100 m increase in elevation. In turn, sage-grouse nest initiation followed SOS by 22 ± 10 days (r2  = .57), with hatch dates falling on or just prior to the peak of the growing season. By timing nesting to the green wave, sage-grouse chicks hatched when the abundance of protein-rich invertebrate biomass is hypothesized to be nearing a seasonal high. This adaptation likely represents a strategy for maximizing reproductive success in the arid, variable environments that define sagebrush ecosystems. Given projected changes in climate and land use, these results can be used to predict periods of relative sensitivity to habitat disturbance for sage-grouse. Moreover, the near real-time availability of satellite imagery offers a heretofore underutilized means of mapping the green wave, planning habitat restoration, and monitoring range conditions.

Residential Greenness and Frailty Among Older Adults: A Longitudinal Cohort in China

OBJECTIVES

Frailty is an accumulation of deficits characterized by reduced resilience to stressors and increased vulnerability to adverse outcomes. There is evolving evidence on the health benefits of residential greenness, but little is known about its impact on frailty.

DESIGN

A longitudinal cohort study.

SETTING AND PARTICIPANTS

We included older adults aged ≥65 years from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) with a 12-year follow-up.

METHODS

We assessed residential greenness by calculating the Normalized Difference Vegetation Index (NDVI) in the 500 m radius around participants' residence. We used 39 self-reported health items to construct a frailty index (FI) as a proportion of accumulated deficits. We defined an FI of ≤0.21 as nonfrail and prefrail, and an FI of >0.21 as frail. We used the mixed effects logistic regression models to examine the association between residential greenness and frailty, adjusted for a number of covariates.

RESULTS

We had 16,238 participants, with a mean age of 83.0 years (standard deviation: 11.5). The mean baseline NDVI and FI were 0.40, and 0.12, respectively. Compared to the participants living in the lowest quartile of residential greenness, those in the highest quartile had a 14% [odds ratio (OR): 0.86, 95% confidence interval (CI): 0.77, 0.97] lower odds of frailty. The association was stronger among urban vs rural residents. Additionally, each 0.1-unit increase in annual average NDVI was related to a 2% higher odds of improvement in the frailty status (OR: 1.02, 95% CI: 1.00, 1.04).

CONCLUSIONS AND IMPLICATIONS

Our study suggests that higher levels of residential greenness are related to a lower likelihood of frailty, specifically in urban areas.

NDVI Changes Show Warming Increases the Length of the Green Season at Tundra Communities in Northern Alaska: A Fine-Scale Analysis

A warming Arctic has been associated with increases in aboveground plant biomass, specifically shrubs, and changes in vegetation cover. However, the magnitude and direction of changes in NDVI have not been consistent across different tundra types. Here we examine the responsiveness of fine-scale NDVI values to experimental warming at eight sites in northern Alaska, United States. Warming in our eight sites ranged in duration from 2‑23 seasons. Dry, wet and moist tundra communities were monitored for canopy surface temperatures and NDVI in ambient and experimentally-warmed plots at near-daily frequencies during the summer of 2017 to assess the impact of the warming treatment on the magnitude and timing of greening. Experimental warming increased canopy-level surface temperatures across all sites (+0.47 to +3.14˚C), with the strongest warming effect occurring during June and July and for the southernmost sites. Green-up was accelerated by warming at six sites, and autumn senescence was delayed at five sites. Warming increased the magnitude of peak NDVI values at five sites, decreased it at one site, and at two sites it did not change. Warming resulted in earlier peak NDVI at three sites and no significant change in the other sites. Shrub and graminoid cover was positively correlated with the magnitude of peak NDVI (r=0.37 to 0.60) while cryptogam influence was mixed. The magnitude and timing of peak NDVI showed considerable variability across sites. Warming extended the duration of the summer green season at most sites due to accelerated greening in the spring and delayed senescence in the autumn. We show that in a warmer Arctic (as simulated by our experiment) the timing and total period of carbon gain may change. Our results suggest these changes are dependent on community composition and abundance of specific growth forms and therefore will likely impact net primary productivity and trophic interactions.

Changes to soil conservation in the Three Gorges Reservoir Area between 1982 and 2015

Soil erosion is a major threat in the Three Gorges Reservoir Area (TGRA) of China. Since 1990, the Chinese government has launched a series of ecological restoration projects to promote soil conservation in the TGRA. To understand the effects of ecological restoration on soil conservation in the TGRA, we used the abrupt change analysis of soil mass from 1982 to 2015 and its drivers; soil mass was obtained with the universal soil loss equation at continental scale. We found that soil conservation and annual rainfall decreased in the TGRA over the study period. Abrupt change points of soil conservation occurred in 1984 and 2007. Soil conservation in the TGRA showed a dramatic decrease before 1984, a slow increase after 1984 as a result of climate, and a rapid increase after 2007 due to an increase in vegetation cover. From 1982 to 2015, climate change played a primary role in soil conservation changes and was more influential than topography and vegetation. However, ecological restoration was an important factor affecting soil conservation in the TGRA, and it needs to be promoted.

A One Health perspective to identify environmental factors that affect Rift Valley fever transmission in Gezira state, Central Sudan

BACKGROUND

Rift Valley fever (RVF) is a zoonotic viral vector-borne disease that affects both animals and humans and leads to severe economic consequences. RVF outbreaks are triggered by a favorable environment and flooding, which enable mosquitoes to proliferate and spread the virus further. RVF is endemic to Africa and has spread to Saudi Arabia and Yemen. There is great concern that RVF may spread to previously unaffected geographic regions due to climate change. We aimed to better understand the spatiotemporal pattern of the 2007 RVF outbreak at the human-animal-environment interface and to determine environmental factors that may have effects on RVF occurrence in Gezira state, Sudan.

MATERIALS AND METHODS

We compiled epidemiological, environmental, and spatiotemporal data across time and space using remote sensing and a geographical information system (GIS). The epidemiological data included 430 RVF human cases as well as human and animal population demographic data for each locality. The cases were collected from 41 locations in Gezira state. The environmental data represent classified land cover during 2007, the year of the RVF outbreak, and the average of the Normalized Difference Vegetation Index (NDVI) for 6 months of 2007 is compared with those of 2010 and 2014, when there was no RVF outbreak. To determine the effect of the environmental factors such as NDVI, soil type, and RVF case's location on the Blue Nile riverbank on RVF incidence in Gezira state, a multilevel logistic regression model was carried out.

RESULTS

We found that the outbreak in Gezira state occurred as a result of interaction among animals, humans, and the environment. The multilevel logistic regression model (F = 43,858, df = 3, p = 0.000) explained 23% of the variance in RVF incidence due to the explanatory variables. Notably, soil type (β = 0.613, t = 11.284, p = 0.000) and NDVI (β = - 0.165, t = - 3.254, p = 0.001) were the explanatory environmental factors that had significant effects on RVF incidence in 2007 in Gezira state, Sudan.

CONCLUSIONS

Precise remote sensing and the GIS technique, which rely on environmental indices such as NDVI and soil type that are satellite-derived, can contribute to establishing an early warning system for RVF in Sudan.Future preparedness and strengthening the capacity of regional laboratories are necessary for early notification of outbreaks in animals and humans.

Interpretation of vegetation phenology changes using daytime and night-time temperatures across the Yellow River Basin, China

Vegetation phenology is highly sensitive to climate change and has a crucial effect on the carbon balance. Prior studies have mainly investigated the effects of mean temperature and precipitation on phenology. The asymmetric and opposing response of phenology to daytime and night-time temperature remains largely unknown. Using the satellite phenology derived from GIMMS NDVI_3g datasets dating back to the 1980s, we show that significantly advanced start of the season (SOS), delayed end of the season (EOS) and prolonged length of growing season (LEN) (P < 0.05) has been taking place in the Yellow River Basin in China. The extension of LEN was more attributed to the advance of SOS than a delayed EOS. The daytime T_max and night-time T_min had opposite effects on the timing of SOS, MOS, and EOS in 63.1%,40.0%, and 53.5% of the pixels of the study area, respectively. If higher T_max leads to an earlier or later transition date, an increase in T_min systematically leads to an opposite effect. These opposite effects were obvious in SOS of 70.5%, 66.2%, and 70.6% of shrubland, grassland, and crop fields, respectively. For EOS, the opposing effects accounting for 58.2%,60.2%, and 54.5% of forest land, shrubland, and grassland, respectively. These results reveal different impacts of climate change on the daytime and night-time carbon cycle in terrestrial ecosystems, and such impacts vary with the land surface type. Knowledge of these opposing responses of phenology to daytime and night-time warming may help to understand the feedback of terrestrial ecosystem structure and function to climate change, thus to improve the existing terrestrial ecosystem carbon cycle model, which is of great significance for climate change and ecology research.

Interactions between Ambient Air Particles and Greenness on Cause-specific Mortality in Seven Korean Metropolitan Cities, 2008-2016

This study aims to investigate the association of particulate matter with an aerodynamic diameter smaller than 10 μm (PM₁₀) and greenness with cause-specific mortality and their interactions in seven Korean metropolitan cities. We obtained the annual standardized cause-specific mortality rates, annual mean concentration of PM₁₀, and annual Normalized Difference Vegetation Index (NDVI) for 73 districts for the period 2008-2016. We used negative binomial regression with city-specific random effects to estimate the association of PM₁₀ and greenness with mortality. The models were adjusted for potential confounders and spatial autocorrelation. We also conducted stratified analyses to investigate whether the association between PM₁₀ and mortality differs by the level of greenness. Our findings suggest an increased risk of all causes examined, except respiratory disease mortality, with high levels of PM₁₀ and decreased risk of cardiovascular-related mortality with a high level of greenness. In the stratified analyses, we found interactions between PM₁₀ and greenness, but these interactions in the opposite direction depend on the cause of death. The effects of PM₁₀ on cardiovascular-related mortality were attenuated in greener areas, whereas the effects of PM₁₀ on non-accidental mortality were attenuated in less green areas. Further studies are needed to explore the underlying mechanisms.

Spatiotemporal variations of vegetation and its determinants in the National Key Ecological Function Area on Loess Plateau between 2000 and 2015

China defined 25 National Key Ecological Function Areas in 2010 and adopted various measures to support ecosystem restoration in these areas. During the process of environment policymaking, it is important to observe the variation of vegetation and its driving factors. In this paper, we chose the National Key Ecological Function Area (NKEFA) on Loess Plateau as the study area. Based on MODIS-NDVI data between 2000 and 2015, the trend analysis was used to depict the change in NDVI and the stepwise regression analysis method was used to quantitatively assess its determinants. The results show that: (a) The vegetation coverage in study area was low in the northwest and high in the southeast, corresponding to the distribution of precipitation and temperature. (b) NDVI in the growing season increased remarkably from 0.2841 in 2000 to 0.4199 in 2015 with a linear tendency of 0.085/10a. About 71.22% of the study area experienced an extremely significant increasing of NDVI, while only 0.03% of the total area suffered from significant decreasing of NDVI. (c) Compared to climatic factors, ecosystem conservation policies, and labor transfer contributed more to the vegetation changes in the study area. In order to ensure ecological security and sustainable development in these areas, it is necessary to maintain the continuity of ecological compensation policy. Moreover, developing targeted eco-compensation policies and encouraging farmers to participate in nonfarm employment are effective ways to reach a win-win outcome of reducing the ecosystem pressure and improving the welfare of rural households.

Greenspace, physical activity, and BMI in children from two cities in northern Mexico

Numerous previous studies have reported positive associations between exposure to greenspace and children's physical activity, but in high-income countries only. Prior studies have also examined greenspace and obesity in children, but these have yielded inconsistent results and focused mostly on older children. The purpose of this study was to assess associations between time children spent in greenspace as the primary exposure and our outcomes of interest, including 1) minutes of physical activity, and 2) body mass index (BMI) z-score. Our sample was 102 children ages 3 to 5 years living in Ensenada and Tijuana, Mexico. We fit linear mixed models to estimate associations between greenspace and children's physical activity and BMI z-score. After adjustment for potential confounders, greater time in greenspace was associated with decreased sedentary time (−0.08 min per hour for each additional 30 min in greenspace; 95% CI −0.13, −0.04; p = 0.002) and increased moderate-to-vigorous physical activity (MVPA) (0.06; 95% CI 0.03, 0.10; p < 0.001). Results were driven primarily by children in Tijuana (−0.22; 95% CI −0.38, −0.06; p = 0.008 for sedentary time and 0.15; 95% CI 0.06, 0.38; p = 0.007 for MVPA). Time in greenspace was not associated with BMI z-score in children in Ensenada (0.001; 95% CI −0.008, 0.01; p = 0.83) or Tijuana (−0.009; 95% CI −0.02, 0.004; p = 0.17). Greater time in greenspace was associated with physical activity but not BMI in our sample of children—more so in Tijuana compared to Ensenada. Given high rates of obesity, interventions should aim to increase physical activity in young children in Northern Mexico.

•

Greater time in greenspace was associated with decreased sedentary time.

•

Greater time in greenspace was associated with increased physical activity.

•

Associations were mainly driven by children in Tijuana compared to Ensenada.

•

Time spent in greenspace was not associated with body mass index z-score.

Association between Urban Greenness and Depressive Symptoms: Evaluation of Greenness Using Various Indicators

An increasing number of studies have suggested benefits of greenness exposure on mental health. We examined the association between urban greenness and depressive symptoms in adults in the general population living in the seven major cities in Korea (N = 65,128). Using data from the Korean Community Health Survey 2009, depressive symptoms were measured on the Center for Epidemiological Studies Depression Scale (CES-D). Greenness was assessed using Normalized Difference Vegetation Index (NDVI) and land-use data (forest area and forest volume). Logistic regression models were fitted to adjust for potential confounders. Individuals in regions with the highest NDVI (quartile 4) had the lowest odds for depressive symptoms compared to quartile 1, after adjusting for potential confounders (OR = 0.813; 95% CI: 0.747, 0.884). For all greenness indicators except for forest area per district area (%), the highest rate of depressive symptoms was found for the individuals in the lowest quartile of greenness (quartile 1) and the lowest rate of depressive symptoms for those in the highest quartile of greenness (quartile 4). We found an inverse association between urban greenness and depressive symptoms, which was consistent across a variety of greenness indicators. Our study suggests health benefits of greenness and could provide a scientific basis for policy making and urban planning.

Vegetation on mesic loamy and sandy soils along a 1700-km maritime Eurasia Arctic Transect

QUESTIONS

How do plant communities on zonal loamy vs. sandy soils vary across the full maritime Arctic bioclimate gradient? How are plant communities of these areas related to existing vegetation units of the European Vegetation Classification? What are the main environmental factors controlling transitions of vegetation along the bioclimate gradient?

LOCATION

1700-km Eurasia Arctic Transect (EAT), Yamal Peninsula and Franz Josef Land (FJL), Russia.

METHODS

The Braun-Blanquet approach was used to sample mesic loamy and sandy plots on 14 total study sites at six locations, one in each of the five Arctic bioclimate subzones and the forest-tundra transition. Trends in soil factors, cover of plant growth forms (PGFs) and species diversity were examined along the summer warmth index (SWI) gradient and on loamy and sandy soils. Classification and ordination were used to group the plots and to test relationships between vegetation and environmental factors.

RESULTS

Clear, mostly non-linear, trends occurred for soil factors, vegetation structure and species diversity along the climate gradient. Cluster analysis revealed seven groups with clear relationships to subzone and soil texture. Clusters at the ends of the bioclimate gradient (forest-tundra and polar desert) had many highly diagnostic taxa, whereas clusters from the Yamal Peninsula had only a few. Axis 1 of a DCA was strongly correlated with latitude and summer warmth; Axis 2 was strongly correlated with soil moisture, percentage sand and landscape age.

CONCLUSIONS

Summer temperature and soil texture have clear effects on tundra canopy structure and species composition, with consequences for ecosystem properties. Each layer of the plant canopy has a distinct region of peak abundance along the bioclimate gradient. The major vegetation types are weakly aligned with described classes of the European Vegetation Checklist, indicating a continuous floristic gradient rather than distinct subzone regions. The study provides ground-based vegetation data for satellite-based interpretations of the western maritime Eurasian Arctic, and the first vegetation data from Hayes Island, Franz Josef Land, which is strongly separated geographically and floristically from the rest of the gradient and most susceptible to on-going climate change.

Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events

Ebola virus disease outbreaks in animals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are little explored. Filoviridae viruses have a wide range of natural hosts and are unstable once outside hosts. Spillover events, which involve the physical transfer of viral particles across species, could therefore be directly promoted by conditions of host ecology and environment. In this report, we outline a proof of concept that temporal fluctuations of a set of ecological and environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We compiled a data set of climate and plant phenology variables and Ebola virus disease spillovers in humans and animals. We identified critical biotic and abiotic conditions for spillovers via multiple regression and neural network-based time series regression. Phenology variables proved to be overall better predictors than climate variables. African phenology variables are not yet available as a comprehensive online resource. Given the likely importance of phenology for forecasting the likelihood of future Ebola spillover events, our results highlight the need for cost-effective transect surveys to supply phenology data for predictive modelling efforts.

Reduction of malaria prevalence after introduction of artemisinin-combination-therapy in Mbeya Region, Tanzania: results from a cohort study with 6773 participants

Background

A marked decline in malaria morbidity and mortality has been reported after the introduction of artemisinin-based combination therapy (ACT) in high malaria prevalence countries in Africa. Data on the impact of ACT and on the prevalence of malaria has so far been scarce for Southwest Tanzania.

Methods

Between 2005 and 2011, a large general population cohort in the Mbeya Region in the south-west of Tanzania has been surveyed within the EMINI-study (Evaluation and Monitoring of the Impact of New Interventions). Participants were examined once per year, including rapid diagnostic testing for malaria. ACT was introduced in the region according to national guidelines in the time period 2006/2007, replacing sulfadoxine/pyrimethamine as first-line therapy. In four study sites, 6773 individuals who participated in the first two of three consecutive survey visits in the period from 2006 to 2009 were included in this analysis. The prevalence of Plasmodium infection prior to and after the introduction of ACT was compared by logistic regression, with consideration of climatic variability, age, sex, socio-economic status and bed net use as potential confounders.

Results

A significant reduction over time in the prevalence of Plasmodium falciparum infection from 2.5 to 0.3% was shown across the four study sites. The decline was not explained by other factors included in the analysis, therefore, the decline over time most likely reflects the impact of introduction of ACT in the study area.

Conclusions

The longitudinal study showed a significant and relevant decline in the prevalence of P. falciparum infection after introduction of ACT, which could not be explained by potential confounders. The data suggests that artemisinin-based combinations are not only an effective instrument for reduction of immediate morbidity and mortality, but also for reduction of transmission rates.

Spatiotemporal analysis of the effect of climate change on vegetation health in the Drakensberg Mountain Region of South Africa

The impact of climate change on mountain ecosystems has been in the spotlight for the past three decades. Climate change is generally considered to be a threat to ecosystem health in mountain regions. Vegetation indices can be used to detect shifts in ecosystem phenology and climate change in mountain regions while satellite imagery can play an important role in this process. However, what has remained problematic is determining the extent to which ecosystem phenology is affected by climate change under increasingly warming conditions. In this paper, we use climate and vegetation indices that were derived from satellite data to investigate the link between ecosystem phenology and climate change in the Namahadi Catchment Area of the Drakensberg Mountain Region of South Africa. The time series for climate indices as well as those for gridded precipitation and temperature data were analyzed in order to determine climate shifts, and concomitant changes in vegetation health were assessed in the resultant epochs using vegetation indices. The results indicate that vegetation indices should only be used to assess trends in climate change under relatively pristine conditions, where human influence is limited. This knowledge is important for designing climate change monitoring strategies that are based on ecosystem phenology and vegetation health.

Potentials and limitations of remote fire monitoring in protected areas

Protected areas (PAs) play an important role in maintaining the biodiversity and ecological processes of the site. One of the greatest challenges for the PA management in several biomes in the world is wildfires. The objective of this work was to evaluate the potentialities and limitations of the use of data obtained by orbital remote sensing in the monitoring fire occurrence in PAs. Fire Occurrence Records (FORs) were analyzed in Serra do Brigadeiro State Park, Minas Gerais, Brazil, from 2007 to 2015, using photo interpreted data from TM, ETM⁺ and OLI sensors of the Landsat series and the Hot Spot Database (HSD) from the Brazilian Institute of Space Research - INPE. It was also observed the time of permanence of the scar left by fire on the landscape, through the multitemporal analysis of the behavior of NDVI (Normalized Difference Vegetation Index) and NBR (Normalized Burn Ratio) indexes, before and after the occurrence. The greatest limitation found for the orbital remote monitoring was the presence of clouds in the passage of the sensor in dates close to the occurrence of the fires. The burned area identified by photo interpretation was 54.9% less than the area contained in the FOR. Although the HSD reported fire occurrences in the buffer zone (up to 10km from the Park), no FORs were found at a distance greater than 1100m from the boundaries of the PA. As the main potential of remote sensing, the possibility of identifying burned areas throughout the park and surroundings is highlighted, with low costs and greater accuracy.

Post-drought Resilience After Forest Die-Off: Shifts in Regeneration, Composition, Growth and Productivity

A better understanding on the consequences of drought on forests can be reached by paying special attention to their resilience capacity, i.e., the ability to return to a state similar to pre-drought conditions. Nevertheless, extreme droughts may surpass the threshold for the resilience capacity triggering die-off causing multiple changes at varying spatial and temporal scales and affecting diverse processes (tree growth and regeneration, ecosystem productivity). Combining several methodological tools allows reaching a comprehensive characterization of post-drought forest resilience. We evaluated the changes in the abundance, regeneration capacity (seedling abundance), and radial growth (annual tree rings) of the main tree species. We also assessed if drought-induced reductions in growth and regeneration of the dominant tree species scale-up to drops in vegetation productivity by using the Normalized Difference Vegetation Index (NDVI). We studied two conifer forests located in north-eastern Spain which displayed drought-induced die-off during the last decades: a Scots pine (Pinus sylvestris) forest under continental Mediterranean conditions and a Silver fir (Abies alba) forest under more temperate conditions. We found a strong negative impact of a recent severe drought (2012) on Scots pine growth, whereas the coexisting Juniperus thurifera showed positive trends in basal area increment (0.02 ± 0.003 cm² yr^-1). No Scots pine recruitment was observed in sites with intense die-off, but J. thurifera and Quercus ilex recruited. The 2012 drought event translated into a strong NDVI reduction (32% lower than the 1982-2014 average). In Silver fir we found a negative impact of the 2012 drought on short-term radial growth, whilst long-term growth of Silver fir and the coexisting Fagus sylvatica showed positive trends. Growth rates were higher in F. sylvatica (0.04 ± 0.003 cm² yr^-1) than in A. alba (0.02 ± 0.004 cm² yr^-1). These two species recruited beneath declining and non-declining Silver fir trees. The 2012 drought translated into a strong NDVI reduction which lasted until 2013. The results presented here suggest two different post-drought vegetation pathways. In the Scots pine forest, the higher growth and recruitment rates of J. thurifera correspond to a vegetation shift where Scots pine is being replaced by the drought-tolerant juniper. Conversely, in the Silver fir forest there is an increase of F. sylvatica growth and abundance but no local extinction of the Silver fir. Further research is required to monitor the evolution of these forests in the forthcoming years to illustrate the cumulative impacts of drought on successional dynamics.

Influences of population pressure change on vegetation greenness in China's mountainous areas

Mountainous areas in China account for two‐thirds of the total land area. Due to rapid urbanization, rural population emigration in China's mountainous areas is very significant. This raises the question to which degree such population emigration influences the vegetation greenness in these areas. In this study, 9,753 sample areas (each sample measured about 64 square kilometers) were randomly selected, and the influences of population emigration (population pressure change) on vegetation greenness during 2000–2010 were quantitatively expressed by the multivariate linear regression (MLR) model, using census data under the condition of controlling the natural elements such as climatic and landform factors. The results indicate that the vegetation index in the past 10 years has presented an increasing overall trend, albeit with local decrease in some regions. The combined area of the regions with improved vegetation accounted for 81.7% of the total mountainous areas in China. From 2000 to 2010, the rural population significantly decreased, with most significant decreases in the northern and central areas (17.2% and 16.8%, respectively). In China's mountainous areas and in most of the subregions, population emigration has significant impacts on vegetation change. In different subregions, population decrease differently influenced vegetation greenness, and the marginal effect of population decrease on vegetation change presented obvious differences from north to south. In the southwest, on the premise of controlling other factors, a population decrease by one unit could increase the slope of vegetation change by 16.4%; in contrast, in the southeastern, northern, northeastern, and central area, the proportion was about 15.5%, 10.6%, 9.7%, and 7.5%, respectively, for improving the trend of NDVI variation.

Arctic greening from warming promotes declines in caribou populations

The migratory tundra caribou herds in North America follow decadal population cycles, and browsing from abundant caribou could be expected to counteract the current climate-driven expansion of shrubs in the circumpolar tundra biome. We demonstrate that the sea ice cover in the Arctic Ocean has provided a strong signal for climate-induced changes on the adjacent caribou summer ranges, outperforming other climate indices in explaining the caribou-plant dynamics. We found no evidence of a negative effect of caribou abundance on vegetation biomass. On the contrary, we found a strong bottom-up effect in which a warmer climate related to diminishing sea ice has increased the plant biomass on the summer pastures, along with a paradoxical decline in caribou populations. This result suggests that this climate-induced greening has been accompanied by a deterioration of pasture quality. The shrub expansion in Arctic North America involves plant species with strong antibrowsing defenses. Our results might therefore be an early signal of a climate-driven shift in the caribou-plant interaction from a system with low plant biomass modulated by cyclic caribou populations to a system dominated by nonedible shrubs and diminishing herds of migratory caribou.

Alpine vegetation phenology dynamic over 16years and its covariation with climate in a semi-arid region of China

Vegetation phenology is a sensitive indicator of ecosystem response to climate change, and plays an important role in the terrestrial biosphere. Improving our understanding of alpine vegetation phenology dynamics and the correlation with climate and grazing is crucial for high mountains in arid areas subject to climatic warming. Using a time series of SPOT Normalized Difference Vegetation Index (NDVI) data from 1998 to 2013, the start of the growing season (SOS), end of the growing season (EOS), growing season length (GSL), and maximum NDVI (MNDVI) were extracted using a threshold-based method for six vegetation groups in the Heihe River headwaters. Spatial and temporal patterns of SOS, EOS, GSL, MNDVI, and correlations with climatic factors and livestock production were analyzed. The MNDVI increased significantly in 58% of the study region, whereas SOS, EOS, and GSL changed significantly in <5% of the region. The MNDVI in five vegetation groups increased significantly by a range from 0.045 to 0.075. No significant correlation between SOS and EOS was observed in any vegetation group. The SOS and GSL were highly correlated with temperature in May and April-May, whereas MNDVI was correlated with temperature in August and July-August. The EOS of different vegetation groups was correlated with different climatic variables. Maximum and minimum temperature, accumulated temperature, and effective accumulated temperature showed stronger correlations with phenological metrics compared with those of mean temperature, and should receive greater attention in phenology modeling in the future. Meat and milk production were significantly correlated with the MNDVI of scrub, steppe, and meadow. Although the MNDVI increased in recent years, ongoing monitoring for rangeland degradation is recommended.

Climate and density influence annual survival and movement in a migratory songbird

Assessing the drivers of survival across the annual cycle is important for understanding when and how population limitation occurs in migratory animals. Density‐dependent population regulation can occur during breeding and nonbreeding periods, and large‐scale climate cycles can also affect survival throughout the annual cycle via their effects on local weather and vegetation productivity. Most studies of survival use mark–recapture techniques to estimate apparent survival, but true survival rates remain obscured due to unknown rates of permanent emigration. This is especially problematic when assessing annual survival of migratory birds, whose movement between breeding attempts, or breeding dispersal, can be substantial. We used a multistate approach to examine drivers of annual survival and one component of breeding dispersal (habitat‐specific movements) in a population of American redstarts (Setophaga ruticilla) over 11 years in two adjacent habitat types. Annual survival displayed a curvilinear relation to the Southern Oscillation Index, with lower survival during La Niña and El Niño conditions. Although redstart density had no impact on survival, habitat‐specific density influenced local movements between habitat types, with redstarts being less likely to disperse from their previous year's breeding habitat as density within that habitat increased. This finding was strongest in males and may be explained by conspecific attraction influencing settlement decisions. Survival was lowest in young males, but movement was highest in this group, indicating that apparent survival rates were likely biased low due to permanent emigration. Our findings demonstrate the utility of examining breeding dispersal in mark–recapture studies and complement recent work using spatially explicit models of dispersal probability to obtain greater accuracy in survival estimates.

How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems

Increasing frequency of extreme climate events is likely to impose increased stress on ecosystems and to jeopardize the services that ecosystems provide. Therefore, it is of major importance to assess the effects of extreme climate events on the temporal stability (i.e., the resistance, the resilience, and the variance) of ecosystem properties. Most time series of ecosystem properties are, however, affected by varying data characteristics, uncertainties, and noise, which complicate the comparison of ecosystem stability metrics (ESMs) between locations. Therefore, there is a strong need for a more comprehensive understanding regarding the reliability of stability metrics and how they can be used to compare ecosystem stability globally. The objective of this study was to evaluate the performance of temporal ESMs based on time series of the Moderate Resolution Imaging Spectroradiometer derived Normalized Difference Vegetation Index of 15 global land-cover types. We provide a framework (i) to assess the reliability of ESMs in function of data characteristics, uncertainties and noise and (ii) to integrate reliability estimates in future global ecosystem stability studies against climate disturbances. The performance of our framework was tested through (i) a global ecosystem comparison and (ii) an comparison of ecosystem stability in response to the 2003 drought. The results show the influence of data quality on the accuracy of ecosystem stability. White noise, biased noise, and trends have a stronger effect on the accuracy of stability metrics than the length of the time series, temporal resolution, or amount of missing values. Moreover, we demonstrate the importance of integrating reliability estimates to interpret stability metrics within confidence limits. Based on these confidence limits, other studies dealing with specific ecosystem types or locations can be put into context, and a more reliable assessment of ecosystem stability against environmental disturbances can be obtained.

Functional analysis of normalized difference vegetation index curves reveals overwinter mule deer survival is driven by both spring and autumn phenology

Large herbivore populations respond strongly to remotely sensed measures of primary productivity. Whereas most studies in seasonal environments have focused on the effects of spring plant phenology on juvenile survival, recent studies demonstrated that autumn nutrition also plays a crucial role. We tested for both direct and indirect (through body mass) effects of spring and autumn phenology on winter survival of 2315 mule deer fawns across a wide range of environmental conditions in Idaho, USA. We first performed a functional analysis that identified spring and autumn as the key periods for structuring the among-population and among-year variation of primary production (approximated from 1 km Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index (NDVI)) along the growing season. A path analysis showed that early winter precipitation and direct and indirect effects of spring and autumn NDVI functional components accounted for 45% of observed variation in overwinter survival. The effect size of autumn phenology on body mass was about twice that of spring phenology, while direct effects of phenology on survival were similar between spring and autumn. We demonstrate that the effects of plant phenology vary across ecosystems, and that in semi-arid systems, autumn may be more important than spring for overwinter survival.

Consistent response of vegetation dynamics to recent climate change in tropical mountain regions

Global climate change has emerged as a major driver of ecosystem change. Here, we present evidence for globally consistent responses in vegetation dynamics to recent climate change in the world's mountain ecosystems located in the pan-tropical belt (30°N-30°S). We analyzed decadal-scale trends and seasonal cycles of vegetation greenness using monthly time series of satellite greenness (Normalized Difference Vegetation Index) and climate data for the period 1982-2006 for 47 mountain protected areas in five biodiversity hotspots. The time series of annual maximum NDVI for each of five continental regions shows mild greening trends followed by reversal to stronger browning trends around the mid-1990s. During the same period we found increasing trends in temperature but only marginal change in precipitation. The amplitude of the annual greenness cycle increased with time, and was strongly associated with the observed increase in temperature amplitude. We applied dynamic models with time-dependent regression parameters to study the time evolution of NDVI-climate relationships. We found that the relationship between vegetation greenness and temperature weakened over time or was negative. Such loss of positive temperature sensitivity has been documented in other regions as a response to temperature-induced moisture stress. We also used dynamic models to extract the trends in vegetation greenness that remain after accounting for the effects of temperature and precipitation. We found residual browning and greening trends in all regions, which indicate that factors other than temperature and precipitation also influence vegetation dynamics. Browning rates became progressively weaker with increase in elevation as indicated by quantile regression models. Tropical mountain vegetation is considered sensitive to climatic changes, so these consistent vegetation responses across widespread regions indicate persistent global-scale effects of climate warming and associated moisture stresses.